Anomaly Detection
Detecting defects and anomalies in manufacturing (MVTec AD, VisA).
Anomaly Detection is a key task in industrial inspection. Below you will find the standard benchmarks used to evaluate models, along with current state-of-the-art results.
Benchmarks & SOTA
MVTec AD
MVTec Anomaly Detection Dataset
5,354 high-resolution images across 15 object and texture categories. The gold standard for industrial anomaly detection with pixel-level annotations.
State of the Art
SimpleNet
Research
99.6
auroc
VisA
Visual Anomaly Dataset
10,821 high-resolution images across 12 objects with complex structures. More challenging than MVTec with realistic industrial scenarios.
State of the Art
SimpleNet
Research
95.5
auroc
NEU-DET
NEU Surface Defect Database
1,800 grayscale images of hot-rolled steel strip with 6 defect types: rolled-in scale, patches, crazing, pitted surface, inclusion, scratches.
State of the Art
DefectDet (ResNet)
Research
78.4
map
Severstal Steel Defect
Severstal Steel Defect Detection
12,568 steel sheet images with 4 defect classes from Kaggle competition. Real industrial data from major steel producer.
State of the Art
YOLOv8 (Weld Detection)
Ultralytics
91.2
dice
Weld Defect X-Ray
X-Ray Weld Defect Detection Dataset
Radiographic images of welds with annotations for porosity, slag inclusion, lack of fusion, cracks. Critical for pipeline and structural inspection.
State of the Art
YOLOv8 (Weld Detection)
Ultralytics
87.3
map
KolektorSDD2
Kolektor Surface Defect Dataset 2
3,335 images of electrical commutators with surface defects. Real industrial dataset with challenging small defects.
No results tracked yet
MVTec 3D-AD
MVTec 3D Anomaly Detection Dataset
4,147 high-resolution 3D point cloud scans and RGB images across 10 categories. First comprehensive 3D anomaly detection benchmark.
No results tracked yet